Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability

doi:10.1016/j.bspc.2022.104144

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	Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability
	Xi, Jianing 2; Wang, Dan 1; Yang, Xuebing 4; Zhang, Wensheng 3,4; Huang, Qinghua 2
刊名	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
	2023
卷号	79 页码:9
关键词	Drug recommendation Explainability Traceability Omic data
ISSN号	1746-8094
DOI	10.1016/j.bspc.2022.104144
通讯作者	Xi, Jianing(xjn@nwpu.edu.cn) ; Huang, Qinghua(qhhuang@nwpu.edu.cn)
英文摘要	The application of Artificial Intelligence (AI) on cancer drug recommendation can prompt the development of personalized cancer therapy. However, most of the current AI drug recommendations cannot give explainable inferences, where their prediction procedures are black boxes, and are difficult to earn the trust of doctors or patients. In explainable inference, the key steps during the recommendation procedures can be located easily, facilitating model adjustment for wrong predictions and model generalization for new drugs/samples. In this paper, we analyze the necessity of developing explainable AI drug recommendation, and propose an evaluation metric called traceability rate. The traceability rate is calculated as the proportion of correct predictions that are traceable along the knowledge graph in all the ground truths. We further conduct an experiment on a benchmark drug response dataset to apply the traceability rate as evaluation metric, where the results show a trade-off between model performance and explainability. Therefore, the explainable AI drug recommendation still demands for further improvement to meet the requirement of clinical personalized therapy.
资助项目	National Key Research and Development Program of China[2018AAA0102104] ; National Natural Science Foundation of China[61901322] ; National Natural Science Foundation of China[62071382]
WOS关键词	SENSITIVITY ; INTELLIGENCE
WOS研究方向	Engineering
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000868136200005
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50284]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Xi, Jianing; Huang, Qinghua
作者单位	1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China 2.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 4.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Xi, Jianing,Wang, Dan,Yang, Xuebing,et al. Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2023,79:9.
APA	Xi, Jianing,Wang, Dan,Yang, Xuebing,Zhang, Wensheng,&Huang, Qinghua.(2023).Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,79,9.
MLA	Xi, Jianing,et al."Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 79(2023):9.